[BioC] normalization for custom chip

[Wolfgang Huber]

Hi Inma,

you can check the list of released packages on the Bioconductor webpage, 
and have a look at the package called vsn. In there, you'll also find a 
vignette and some literature references.

library(reposTools)
install.packages2("vsn", develOK=TRUE)
library("vsn")
openVignette("vsn")

Best wishes
  Wolfgang

-------------------------------------
Wolfgang Huber
European Bioinformatics Institute
European Molecular Biology Laboratory
Wellcome Trust Genome Campus
Cambridge CB10 1SD
England
Phone: +44 1223 494642
Http:  www.dkfz.de/abt0840/whuber
-------------------------------------


M Inmaculada Barrasa wrote:
> Hi Hinnerk,
> 
> I am also tryying to normalize a custom chip.
> Could you please tell me what VSN stands for and where can I find that
> method for normalization and a reference for it?
> 
> Thanks a lot
> 
> Inma
> 
> 
> 
> 
> 
> On Fri, 12 Nov 2004, Hinnerk Boriss wrote:
> 
> 
>>_Dear Shibing,
>>_
>>_I do not recommend using house keeping genes for normalization. In several
>>_experiments they turn out being differentially expressed. A better approach
>>_would be to use a normalization method that searches for an invariant set of
>>_genes in the sample. "VSN" and Li & Wong's "invariant set" do that. The
>>_methods have limits though regarding the minimum proportion of not
>>_differentially expressed genes. Below 30% things become difficult. Another
>>_aspect you should be aware of is that a bias in the treatment effect, i.e.
>>_treatment causes either mostly up- or down-regulation of genes, will distort
>>_your normalization. VSN is most robust against this bias.
>>_
>>_Just an idea for you chip design: make a list of all genes that you think
>>_could react to the planned treatment for 70-80% of your probe sets, then
>>_take a random sample from all the remaining genes (of which you have no
>>_prior evidence for differential expression) to design the remaining 20-30%
>>_of the chip. This should get you a way out your normalization problem
>>_typical for custom chips. In fact, you could restrict the invariant set
>>_algorithm to search only in the random selection of genes.
>>_
>>_Cheers,
>>_Hinnerk
>>_
>>_
>>_-----Original Message-----
>>_From: bioconductor-bounces at stat.math.ethz.ch
>>_[mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of Deng, Shibing
>>_Sent: Thursday, November 11, 2004 10:08 PM
>>_To: 'bioconductor at stat.math.ethz.ch'
>>_Subject: [BioC] normalization for custom chip
>>_
>>_Hi,
>>_We are designing a custom Affymetrix chip with about 1700 genes. By design,
>>_a large number of genes on the chip will be differentially expressed between
>>_our treatment and control samples. The assumption for quantile normalization
>>_and other distribution-based normalization methods will not hold for these
>>_chips. To normalize them, we plan to put some "house-keeping" genes or
>>_"invariant" genes covering a wide range of intensities on the chip. We are
>>_not sure how many house-keeping genes we should have to get a good
>>_normalization? I will appreciate your input on this issue.
>>_
>>_Shibing